Region-based Cluster Discrimination for Visual Representation Learning

• URL: http://arxiv.org/abs/2507.20025v1
• Date: Sat, 26 Jul 2025 17:47:09 GMT
• Title: Region-based Cluster Discrimination for Visual Representation Learning
• Authors: Yin Xie, Kaicheng Yang, Xiang An, Kun Wu, Yongle Zhao, Weimo Deng, Zimin Ran, Yumeng Wang, Ziyong Feng, Roy Miles, Ismail Elezi, Jiankang Deng,
• Abstract summary: Region-Aware Cluster Discrimination (RICE) is a novel method that enhances region-level visual and OCR capabilities.<n>RICE consistently outperforms previous methods on tasks, including segmentation, dense detection, and visual perception.
• Score: 30.79223671093668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning visual representations is foundational for a broad spectrum of downstream tasks. Although recent vision-language contrastive models, such as CLIP and SigLIP, have achieved impressive zero-shot performance via large-scale vision-language alignment, their reliance on global representations constrains their effectiveness for dense prediction tasks, such as grounding, OCR, and segmentation. To address this gap, we introduce Region-Aware Cluster Discrimination (RICE), a novel method that enhances region-level visual and OCR capabilities. We first construct a billion-scale candidate region dataset and propose a Region Transformer layer to extract rich regional semantics. We further design a unified region cluster discrimination loss that jointly supports object and OCR learning within a single classification framework, enabling efficient and scalable distributed training on large-scale data. Extensive experiments show that RICE consistently outperforms previous methods on tasks, including segmentation, dense detection, and visual perception for Multimodal Large Language Models (MLLMs). The pre-trained models have been released at https://github.com/deepglint/MVT.

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